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The Android OS has become the most popular mobile operating system leading toa significant increase in the spread of Android malware. Consequently, severalstatic and dynamic analysis systems have been developed to detect Androidmalware. With dynamic analysis, efficient test input generation is needed inorder to trigger the potential run-time malicious behaviours. Most existingdynamic analysis systems employ random-based input generation methods usuallybuilt using the Android Monkey tool. Random-based input generation has severalshortcomings including limited code coverage, which motivates us to explorecombining it with a state-based method in order to improve efficiency. Hence,in this paper, we present a novel hybrid test input generation approachdesigned to improve dynamic analysis on real devices. We implemented the hybridsystem by integrating a random based tool (Monkey) with a state based tool(DroidBot) in order to improve code coverage and potentially uncover moremalicious behaviours. The system is evaluated using 2,444 Android appscontaining 1222 benign and 1222 malware samples from the Android malware genomeproject. Three scenarios, random only, state-based only, and our proposedhybrid approach were investigated to comparatively evaluate their performances.Our study shows that the hybrid approach significantly improved the amount ofdynamic features extracted from both benign and malware samples over thestate-based and commonly used random test input generation method.

Improving Dynamic Analysis of Android Apps Using Hybrid Test Input Generation